Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms

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@Article{KHAN:2024:cscm,

• author = "Adil Khan and Majid Khan and Mohsin Ali and Murad Khan and Asad Ullah Khan and Muhammad Shakeel and Muhammad Fawad and Taoufik Najeh and Yaser Gamil",
• title = "Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms",
• journal = "Case Studies in Construction Materials",
• volume = "20",
• pages = "e02744",
• year = "2024",
• ISSN = "2214-5095",
• DOI = "doi:10.1016/j.cscm.2023.e02744",
• URL = "https://www.sciencedirect.com/science/article/pii/S2214509523009257",
• keywords = "genetic algorithms, genetic programming, Fly ash, Abrasion resistance, Gene expression programming, Multi-expression programming, SHAP",
• abstract = "There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, we used two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70percent of the data, and the remaining 30percent of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was used for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector",

}

Genetic Programming entries for Adil Khan Majid Khan Mohsin Ali Murad Khan Asad Ullah Khan Muhammad Shakeel Muhammad Fawad Taoufik Najeh Yaser Gamil

Citations